C-Index (Documentation for extended WEKA including Ensembles of Hierarchically Nested Dichotomies)

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C

C45Loader - class weka.core.converters.C45Loader.: Reads C4.5 input files.
C45Loader() - Constructor for class weka.core.converters.C45Loader
C45ModelSelection - class weka.classifiers.trees.j48.C45ModelSelection.: Class for selecting a C4.5-type split for a given dataset.
C45ModelSelection(int, Instances) - Constructor for class weka.classifiers.trees.j48.C45ModelSelection: Initializes the split selection method with the given parameters.
C45PruneableClassifierTree - class weka.classifiers.trees.j48.C45PruneableClassifierTree.: Class for handling a tree structure that can be pruned using C4.5 procedures.
C45PruneableClassifierTree(ModelSelection, boolean, float, boolean, boolean) - Constructor for class weka.classifiers.trees.j48.C45PruneableClassifierTree: Constructor for pruneable tree structure.
C45PruneableDecList - class weka.classifiers.rules.part.C45PruneableDecList.: Class for handling a partial tree structure pruned using C4.5's pruning heuristic.
C45PruneableDecList(ModelSelection, double, int) - Constructor for class weka.classifiers.rules.part.C45PruneableDecList: Constructor for pruneable tree structure.
C45Split - class weka.classifiers.trees.j48.C45Split.: Class implementing a C4.5-type split on an attribute.
C45Split(int, int, double) - Constructor for class weka.classifiers.trees.j48.C45Split: Initializes the split model.
CANCEL_OPTION - Static variable in class weka.gui.ListSelectorDialog: Signifies a cancelled property selection
CANCEL_OPTION - Static variable in class weka.gui.PropertySelectorDialog: Signifies a cancelled property selection
CATEGORY_NAMES - Static variable in class weka.classifiers.evaluation.TwoClassStats: The names used when converting this object to a confusion matrix
CF - Variable in class weka.classifiers.rules.part.C45PruneableDecList: CF
CF - Variable in class weka.classifiers.rules.part.MakeDecList: The confidence for C45-type pruning.
CLASSIFYING - Static variable in class weka.gui.beans.Classifier
CLASSIFY_CHILD - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent: Asks for another learning scheme to classify this node.
COMBO_SIZE - Variable in class weka.gui.boundaryvisualizer.BoundaryVisualizer
COMBO_SIZE - Variable in class weka.gui.experiment.ResultsPanel
COMBO_SIZE - Variable in class weka.gui.explorer.AttributeSelectionPanel: Stop the class combo from taking up to much space
COMBO_SIZE - Variable in class weka.gui.explorer.ClassifierPanel: Stop the class combo from taking up to much space
COMBO_SIZE - Variable in class weka.gui.explorer.ClustererPanel: Stop the class combo from taking up to much space
COMBO_SIZE - Variable in class weka.gui.visualize.VisualizePanel: Stop the combos from growing out of control
COMMA - Static variable in class weka.core.ClassTree: Class separator in an tree encoding String or number separator in a range list.
COMPONENTS - Static variable in class weka.gui.beans.BeanInstance: class variable holding all the beans
CONFIDENCE - Static variable in class weka.associations.Apriori: Metric types.
CONNECTED - Static variable in class weka.classifiers.functions.neural.NeuralConnection: This flag is set once the unit has a connection.
CONNECTING - Static variable in class weka.gui.beans.KnowledgeFlow
CONNECTIONS - Static variable in class weka.gui.beans.BeanConnection: The list of connections
CONNECTION_FAILED - Static variable in class weka.experiment.RemoteExperiment
CONNECTION_FAILED - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed
CONST_AUTOMATIC_SHAPE - Static variable in class weka.gui.visualize.Plot2D
CONVICTION - Static variable in class weka.associations.Apriori
CSVDataSink - class weka.gui.beans.CSVDataSink.: Data sink that stores instances to a comma separated values (CSV) text file
CSVDataSink() - Constructor for class weka.gui.beans.CSVDataSink
CSVDataSinkBeanInfo - class weka.gui.beans.CSVDataSinkBeanInfo.: Bean info class for the CSVDataSink bean
CSVDataSinkBeanInfo() - Constructor for class weka.gui.beans.CSVDataSinkBeanInfo
CSVLoader - class weka.core.converters.CSVLoader.: Reads a text file that is comma or tab delimited..
CSVLoader() - Constructor for class weka.core.converters.CSVLoader
CSVResultListener - class weka.experiment.CSVResultListener.: CSVResultListener outputs the received results in csv format to a Writer
CSVResultListener() - Constructor for class weka.experiment.CSVResultListener
CVClusters() - Method in class weka.clusterers.EM: estimate the number of clusters by cross validation on the training data.
CVParameterSelection - class weka.classifiers.meta.CVParameterSelection.: Class for performing parameter selection by cross-validation for any classifier.
CVParameterSelection() - Constructor for class weka.classifiers.meta.CVParameterSelection
CVParameterSelection.CVParameter - class weka.classifiers.meta.CVParameterSelection.CVParameter.
CVParameterSelection.CVParameter(String) - Constructor for class weka.classifiers.meta.CVParameterSelection.CVParameter: Constructs a CVParameter.
CVParametersTipText() - Method in class weka.classifiers.meta.CVParameterSelection: Returns the tip text for this property
CVResultsString() - Method in class weka.attributeSelection.AttributeSelection: returns a string summarizing the results of repeated attribute selection runs on splits of a dataset.
CalcNodeScore(int) - Method in class weka.classifiers.bayes.BayesNet: Calc Node Score for given parent set
CalcNodeScore(int, Instances) - Method in class weka.classifiers.bayes.BayesNet
CalcNodeScoreADTree(int, Instances) - Method in class weka.classifiers.bayes.BayesNet: helper function for CalcNodeScore above using the ADTree data structure
CalcScoreOfCounts(int[], int, int, Instances) - Method in class weka.classifiers.bayes.BayesNet: utility function used by CalcScore and CalcNodeScore to determine the score based on observed frequencies.
CalcScoreOfCounts2(int[][], int, int, Instances) - Method in class weka.classifiers.bayes.BayesNet
CalcScoreWithExtraParent(int, int) - Method in class weka.classifiers.bayes.BayesNet: Calc Node Score With AddedParent
CfsSubsetEval - class weka.attributeSelection.CfsSubsetEval.: CFS attribute subset evaluator.
CfsSubsetEval() - Constructor for class weka.attributeSelection.CfsSubsetEval: Constructor
ChartEvent - class weka.gui.beans.ChartEvent.: Event encapsulating info for plotting a data point on the StripChart
ChartEvent(Object, Vector, double, double, double[], boolean) - Constructor for class weka.gui.beans.ChartEvent: Creates a new ChartEvent instance.
ChartEvent(Object) - Constructor for class weka.gui.beans.ChartEvent: Creates a new ChartEvent instance.
ChartListener - interface weka.gui.beans.ChartListener.: Interface to something that can process a ChartEvent
CheckClassifier - class weka.classifiers.CheckClassifier.: Class for examining the capabilities and finding problems with classifiers.
CheckClassifier() - Constructor for class weka.classifiers.CheckClassifier
CheckOptionHandler - class weka.core.CheckOptionHandler.: Simple command line checking of classes that implement OptionHandler.
CheckOptionHandler() - Constructor for class weka.core.CheckOptionHandler
ChiSquaredAttributeEval - class weka.attributeSelection.ChiSquaredAttributeEval.: Class for Evaluating attributes individually by measuring the chi-squared statistic with respect to the class.
ChiSquaredAttributeEval() - Constructor for class weka.attributeSelection.ChiSquaredAttributeEval: Constructor
ChisqMixture - class weka.classifiers.functions.pace.ChisqMixture.: Class for manipulating chi-square mixture distributions.
ChisqMixture() - Constructor for class weka.classifiers.functions.pace.ChisqMixture: Contructs an empty ChisqMixture
ClassAssigner - class weka.gui.beans.ClassAssigner.: Describe class ClassAssigner here.
ClassAssigner() - Constructor for class weka.gui.beans.ClassAssigner
ClassAssignerBeanInfo - class weka.gui.beans.ClassAssignerBeanInfo.: BeanInfo class for the class assigner bean
ClassAssignerBeanInfo() - Constructor for class weka.gui.beans.ClassAssignerBeanInfo
ClassAssignerCustomizer - class weka.gui.beans.ClassAssignerCustomizer.: GUI customizer for the class assigner bean
ClassAssignerCustomizer() - Constructor for class weka.gui.beans.ClassAssignerCustomizer
ClassHierarchy - interface weka.core.ClassHierarchy.: Interface for representations of a hierarchy of classes.
ClassHierarchyParser - interface weka.core.converters.ClassHierarchyParser.: Interface defines required methods for all parsers, which provide a ClassHierarchy.
ClassOrder - class weka.filters.supervised.attribute.ClassOrder.: A filter that sorts the order of classes so that the class values are no longer of in the order of that in the header file after filtered.
ClassOrder() - Constructor for class weka.filters.supervised.attribute.ClassOrder
ClassPanel - class weka.gui.visualize.ClassPanel.: This panel displays coloured labels for nominal attributes and a spectrum for numeric attributes.
ClassPanel() - Constructor for class weka.gui.visualize.ClassPanel
ClassPanel.NomLabel - class weka.gui.visualize.ClassPanel.NomLabel.: Inner Inner class used to create labels for nominal attributes so that there color can be changed.
ClassPanel.NomLabel(String, int) - Constructor for class weka.gui.visualize.ClassPanel.NomLabel: Creates a label with its name and class index value.
ClassRemoveableInstances - class weka.core.ClassRemoveableInstances.
ClassRemoveableInstances(Reader) - Constructor for class weka.core.ClassRemoveableInstances
ClassRemoveableInstances(Reader, int) - Constructor for class weka.core.ClassRemoveableInstances
ClassRemoveableInstances(Instances) - Constructor for class weka.core.ClassRemoveableInstances
ClassRemoveableInstances(Instances, int) - Constructor for class weka.core.ClassRemoveableInstances
ClassRemoveableInstances(Instances, int, int) - Constructor for class weka.core.ClassRemoveableInstances
ClassRemoveableInstances(String, FastVector, int) - Constructor for class weka.core.ClassRemoveableInstances
ClassTree - class weka.core.ClassTree.: ClassTree provides the hierarchy of classes according to a given encoding.
ClassTree(String[]) - Constructor for class weka.core.ClassTree: Constructs a bottom-end class-tree containing only leaves.
ClassTree(ClassTree[]) - Constructor for class weka.core.ClassTree: Constructs a class-tree containing the given subtrees as children.
ClassTreeArffFileParser - class weka.core.converters.ClassTreeArffFileParser.: Works as a ClassTreeFileParser, but expects the file to be of arff-format and to contain a comment-line providing as keyword "@hierarchy" followed by a hierarchy-string suitable for ClassTreeParser.setEncodedHierarchy(String encodedHierarchy).
ClassTreeArffFileParser() - Constructor for class weka.core.converters.ClassTreeArffFileParser
ClassTreeFileParser - class weka.core.converters.ClassTreeFileParser.: Works as ClassTreeParser, but expects the encodedHierarchy to be provided by a file's first line.
ClassTreeFileParser() - Constructor for class weka.core.converters.ClassTreeFileParser
ClassTreeParser - class weka.core.converters.ClassTreeParser.: ClassTreeParser is a ClassHierarchyParser which provides as ClassHierarchy-structure a ClassTree
ClassTreeParser() - Constructor for class weka.core.converters.ClassTreeParser
ClassificationViaRegression - class weka.classifiers.meta.ClassificationViaRegression.: Class for doing classification using regression methods.
ClassificationViaRegression() - Constructor for class weka.classifiers.meta.ClassificationViaRegression: Default constructor.
Classifier - class weka.classifiers.Classifier.: Abstract classifier.
Classifier() - Constructor for class weka.classifiers.Classifier
Classifier - class weka.gui.beans.Classifier.: Bean that wraps around weka.classifiers
Classifier() - Constructor for class weka.gui.beans.Classifier: Creates a new Classifier instance.
ClassifierBeanInfo - class weka.gui.beans.ClassifierBeanInfo.: BeanInfo class for the Classifier wrapper bean
ClassifierBeanInfo() - Constructor for class weka.gui.beans.ClassifierBeanInfo
ClassifierCustomizer - class weka.gui.beans.ClassifierCustomizer.: GUI customizer for the classifier wrapper bean
ClassifierCustomizer() - Constructor for class weka.gui.beans.ClassifierCustomizer
ClassifierDecList - class weka.classifiers.rules.part.ClassifierDecList.: Class for handling a rule (partial tree) for a decision list.
ClassifierDecList(ModelSelection, int) - Constructor for class weka.classifiers.rules.part.ClassifierDecList: Constructor - just calls constructor of class DecList.
ClassifierPanel - class weka.gui.explorer.ClassifierPanel.: This panel allows the user to select and configure a classifier, set the attribute of the current dataset to be used as the class, and evaluate the classifier using a number of testing modes (test on the training data, train/test on a percentage split, n-fold cross-validation, test on a separate split).
ClassifierPanel() - Constructor for class weka.gui.explorer.ClassifierPanel: Creates the classifier panel
ClassifierPerformanceEvaluator - class weka.gui.beans.ClassifierPerformanceEvaluator.: A bean that evaluates the performance of batch trained classifiers
ClassifierPerformanceEvaluator() - Constructor for class weka.gui.beans.ClassifierPerformanceEvaluator
ClassifierPerformanceEvaluatorBeanInfo - class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo.: Bean info class for the classifier performance evaluator
ClassifierPerformanceEvaluatorBeanInfo() - Constructor for class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo
ClassifierSplitEvaluator - class weka.experiment.ClassifierSplitEvaluator.: A SplitEvaluator that produces results for a classification scheme on a nominal class attribute.
ClassifierSplitEvaluator() - Constructor for class weka.experiment.ClassifierSplitEvaluator: No args constructor.
ClassifierSplitModel - class weka.classifiers.trees.j48.ClassifierSplitModel.: Abstract class for classification models that can be used recursively to split the data.
ClassifierSplitModel() - Constructor for class weka.classifiers.trees.j48.ClassifierSplitModel
ClassifierSubsetEval - class weka.attributeSelection.ClassifierSubsetEval.: Classifier subset evaluator.
ClassifierSubsetEval() - Constructor for class weka.attributeSelection.ClassifierSubsetEval
ClassifierTree - class weka.classifiers.trees.j48.ClassifierTree.: Class for handling a tree structure used for classification.
ClassifierTree(ModelSelection) - Constructor for class weka.classifiers.trees.j48.ClassifierTree: Constructor.
ClusterEvaluation - class weka.clusterers.ClusterEvaluation.: Class for evaluating clustering models.
ClusterEvaluation() - Constructor for class weka.clusterers.ClusterEvaluation: Constructor.
ClusterGenerator - class weka.datagenerators.ClusterGenerator.: Abstract class for cluster data generators. ------------------------------------------------------------------- General options are: -r string
Name of the relation of the generated dataset.
ClusterGenerator() - Constructor for class weka.datagenerators.ClusterGenerator
ClusterMembership - class weka.filters.unsupervised.attribute.ClusterMembership.: A filter that uses a clusterer to obtain cluster membership probabilites for each input instance and outputs them as new instances.
ClusterMembership() - Constructor for class weka.filters.unsupervised.attribute.ClusterMembership
Clusterer - class weka.clusterers.Clusterer.: Abstract clusterer.
Clusterer() - Constructor for class weka.clusterers.Clusterer
ClustererPanel - class weka.gui.explorer.ClustererPanel.: This panel allows the user to select and configure a clusterer, and evaluate the clusterer using a number of testing modes (test on the training data, train/test on a percentage split, test on a separate split).
ClustererPanel() - Constructor for class weka.gui.explorer.ClustererPanel: Creates the clusterer panel
Cobweb - class weka.clusterers.Cobweb.: Class implementing the Cobweb and Classit clustering algorithms.
Cobweb() - Constructor for class weka.clusterers.Cobweb
Cobweb.CNode - class weka.clusterers.Cobweb.CNode.: Inner class handling node operations for Cobweb.
Cobweb.CNode(int) - Constructor for class weka.clusterers.Cobweb.CNode: Creates an empty CNode instance.
Cobweb.CNode(int, Instance) - Constructor for class weka.clusterers.Cobweb.CNode: Creates a new leaf CNode instance.
Colors - class weka.gui.treevisualizer.Colors.: This class maintains a list that contains all the colornames from the dotty standard and what color (in RGB) they represent
Colors() - Constructor for class weka.gui.treevisualizer.Colors
CompareNode - class weka.classifiers.trees.lmt.CompareNode.: Auxiliary class for list of LMTNodes
CompareNode() - Constructor for class weka.classifiers.trees.lmt.CompareNode
ComplementNaiveBayes - class weka.classifiers.bayes.ComplementNaiveBayes.: Class for building and using a Complement class Naive Bayes classifier.
ComplementNaiveBayes() - Constructor for class weka.classifiers.bayes.ComplementNaiveBayes
Compute - interface weka.experiment.Compute.: Interface to something that can accept remote connections and execute a task.
ConditionalEstimator - interface weka.estimators.ConditionalEstimator.: Interface for conditional probability estimators.
ConfusionMatrix - class weka.classifiers.evaluation.ConfusionMatrix.: Cells of this matrix correspond to counts of the number (or weight) of predictions for each actual value / predicted value combination.
ConfusionMatrix(String[]) - Constructor for class weka.classifiers.evaluation.ConfusionMatrix: Creates the confusion matrix with the given class names.
ConjunctiveRule - class weka.classifiers.rules.ConjunctiveRule.: This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
ConjunctiveRule() - Constructor for class weka.classifiers.rules.ConjunctiveRule
ConjunctiveRule.Antd - class weka.classifiers.rules.ConjunctiveRule.Antd.: The single antecedent in the rule, which is composed of an attribute and the corresponding value.
ConjunctiveRule.Antd(Attribute, double[]) - Constructor for class weka.classifiers.rules.ConjunctiveRule.Antd: Constructor for nominal class
ConjunctiveRule.Antd(Attribute, double, double, double) - Constructor for class weka.classifiers.rules.ConjunctiveRule.Antd
ConjunctiveRule.NominalAntd - class weka.classifiers.rules.ConjunctiveRule.NominalAntd.: The antecedent with nominal attribute
ConjunctiveRule.NominalAntd(Attribute, double[]) - Constructor for class weka.classifiers.rules.ConjunctiveRule.NominalAntd
ConjunctiveRule.NominalAntd(Attribute, double, double, double) - Constructor for class weka.classifiers.rules.ConjunctiveRule.NominalAntd
ConjunctiveRule.NumericAntd - class weka.classifiers.rules.ConjunctiveRule.NumericAntd.: The antecedent with numeric attribute
ConjunctiveRule.NumericAntd(Attribute, double[]) - Constructor for class weka.classifiers.rules.ConjunctiveRule.NumericAntd
ConjunctiveRule.NumericAntd(Attribute, double, double, double) - Constructor for class weka.classifiers.rules.ConjunctiveRule.NumericAntd
ConsistencySubsetEval - class weka.attributeSelection.ConsistencySubsetEval.: Consistency attribute subset evaluator.
ConsistencySubsetEval() - Constructor for class weka.attributeSelection.ConsistencySubsetEval: Constructor.
ConsistencySubsetEval.hashKey - class weka.attributeSelection.ConsistencySubsetEval.hashKey.: Class providing keys to the hash table.
ConsistencySubsetEval.hashKey(Instance, int) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey: Constructor for a hashKey
ConsistencySubsetEval.hashKey(double[]) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey: Constructor for a hashKey
ContingencyTables - class weka.core.ContingencyTables.: Class implementing some statistical routines for contingency tables.
ContingencyTables() - Constructor for class weka.core.ContingencyTables
ConverterUtils - class weka.core.converters.ConverterUtils.: Utility routines for the converter package.
ConverterUtils() - Constructor for class weka.core.converters.ConverterUtils
Copy - class weka.filters.unsupervised.attribute.Copy.: An instance filter that copies a range of attributes in the dataset.
Copy() - Constructor for class weka.filters.unsupervised.attribute.Copy
Copyable - interface weka.core.Copyable.: Interface implemented by classes that can produce "shallow" copies of their objects.
CorrelationSplitInfo - class weka.classifiers.trees.m5.CorrelationSplitInfo.: Finds split points using correlation.
CorrelationSplitInfo(int, int, int) - Constructor for class weka.classifiers.trees.m5.CorrelationSplitInfo: Constructs an object which contains the split information
CostCurve - class weka.classifiers.evaluation.CostCurve.: Generates points illustrating probablity cost tradeoffs that can be obtained by varying the threshold value between classes.
CostCurve() - Constructor for class weka.classifiers.evaluation.CostCurve
CostMatrix - class weka.classifiers.CostMatrix.: Class for storing and manipulating a misclassification cost matrix.
CostMatrix(CostMatrix) - Constructor for class weka.classifiers.CostMatrix: Creates a cost matrix that is a copy of another.
CostMatrix(int) - Constructor for class weka.classifiers.CostMatrix: Creates a default cost matrix of a particular size.
CostMatrix(Reader) - Constructor for class weka.classifiers.CostMatrix: Creates a cost matrix from a reader.
CostMatrixEditor - class weka.gui.CostMatrixEditor.: Class for editing CostMatrix objects.
CostMatrixEditor() - Constructor for class weka.gui.CostMatrixEditor: Constructs a new CostMatrixEditor.
CostMatrixEditor.CostMatrixTableModel - class weka.gui.CostMatrixEditor.CostMatrixTableModel.: This class wraps around the cost matrix presenting it as a TableModel so that it can be displayed and edited in a JTable.
CostMatrixEditor.CostMatrixTableModel() - Constructor for class weka.gui.CostMatrixEditor.CostMatrixTableModel
CostMatrixEditor.CustomEditor - class weka.gui.CostMatrixEditor.CustomEditor.: This class presents a GUI for editing the cost matrix, and saving and loading from files.
CostMatrixEditor.CustomEditor() - Constructor for class weka.gui.CostMatrixEditor.CustomEditor: Constructs a new CustomEditor.
CostSensitiveClassifier - class weka.classifiers.meta.CostSensitiveClassifier.: This metaclassifier makes its base classifier cost-sensitive.
CostSensitiveClassifier() - Constructor for class weka.classifiers.meta.CostSensitiveClassifier
CostSensitiveClassifierSplitEvaluator - class weka.experiment.CostSensitiveClassifierSplitEvaluator.: A SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
CostSensitiveClassifierSplitEvaluator() - Constructor for class weka.experiment.CostSensitiveClassifierSplitEvaluator
CramersV(double[][]) - Static method in class weka.core.ContingencyTables: Computes Cramer's V for a contingency table.
CrossValidateAttributes() - Method in class weka.attributeSelection.AttributeSelection: Perform a cross validation for attribute selection.
CrossValidationFoldMaker - class weka.gui.beans.CrossValidationFoldMaker.: Bean for splitting instances into training ant test sets according to a cross validation
CrossValidationFoldMaker() - Constructor for class weka.gui.beans.CrossValidationFoldMaker
CrossValidationFoldMakerBeanInfo - class weka.gui.beans.CrossValidationFoldMakerBeanInfo.: BeanInfo class for the cross validation fold maker bean
CrossValidationFoldMakerBeanInfo() - Constructor for class weka.gui.beans.CrossValidationFoldMakerBeanInfo
CrossValidationFoldMakerCustomizer - class weka.gui.beans.CrossValidationFoldMakerCustomizer.: GUI Customizer for the cross validation fold maker bean
CrossValidationFoldMakerCustomizer() - Constructor for class weka.gui.beans.CrossValidationFoldMakerCustomizer
CrossValidationResultProducer - class weka.experiment.CrossValidationResultProducer.: Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results.
CrossValidationResultProducer() - Constructor for class weka.experiment.CrossValidationResultProducer
CustomPanelSupplier - interface weka.gui.CustomPanelSupplier.: An interface for objects that are capable of supplying their own custom GUI components.
cTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
cTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
cacheKeyNameTipText() - Method in class weka.experiment.DatabaseResultListener: Returns the tip text for this property
cacheSizeTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
cacheSizeTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
calcClassType(Instance) - Method in class weka.classifiers.trees.UserClassifier.TreeClass: This will recursively go through the tree and return inside the array the weightings of each of the class types for this instance.
calcColBC(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: See Sugiyama et al. 1981 (full reference give at top)
calcGraph() - Method in class weka.gui.AttributeVisualizationPanel
calcOutOfBagTipText() - Method in class weka.classifiers.meta.Bagging: Returns the tip text for this property
calcPredictionValue(Instances, Instances) - Method in class weka.classifiers.trees.ADTree: Calculates the prediction value used for a particular set of instances.
calcRowBC(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: See Sugiyama et al. 1981 (full reference give at top)
calcScreenCoords(int) - Method in class weka.gui.treevisualizer.TreeVisualizer: Converts the internal coordinates of the node found from n and converts them to the actual screen coordinates.
calcZpure(Instances, Instances) - Method in class weka.classifiers.trees.ADTree: Calculates the Z-pure value for a particular set of instances.
calculateAlphas() - Method in class weka.classifiers.trees.lmt.LMTNode: Updates the alpha field for all nodes.
calculateConfirmation() - Method in class weka.associations.tertius.Rule: Calculate the confirmation of this rule.
calculateCovariance() - Method in class weka.estimators.NNConditionalEstimator: Calculate covariance and value means
calculateCutPoints() - Method in class weka.filters.supervised.attribute.Discretize: Generate the cutpoints for each attribute
calculateCutPoints() - Method in class weka.filters.unsupervised.attribute.Discretize: Generate the cutpoints for each attribute
calculateCutPointsByEqualFrequencyBinning(int) - Method in class weka.filters.unsupervised.attribute.Discretize: Set cutpoints for a single attribute.
calculateCutPointsByEqualWidthBinning(int) - Method in class weka.filters.unsupervised.attribute.Discretize: Set cutpoints for a single attribute.
calculateCutPointsByMDL(int, Instances) - Method in class weka.filters.supervised.attribute.Discretize: Set cutpoints for a single attribute using MDL.
calculateDerived() - Method in class weka.experiment.PairedStats: Calculates the derived statistics (significance etc).
calculateDerived() - Method in class weka.experiment.PairedStatsCorrected: Calculates the derived statistics (significance etc).
calculateDerived() - Method in class weka.experiment.Stats: Tells the object to calculate any statistics that don't have their values automatically updated during add.
calculateEntropy(double, KStarWrapper) - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute: Calculates the entropy of the actual class prediction and the entropy for random class prediction.
calculateEntropy(double, KStarWrapper) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Calculates several parameters aside from the entropy: for a specified scale factor, calculates the actual entropy, a random entropy using a randomized set of class value colomns, and records the average and smallest probabilities (for use in missing value case).
calculateErrors() - Method in class weka.classifiers.functions.MultilayerPerceptron: This will cause the error values to be calculated for all nodes.
calculateOptimistic() - Method in class weka.associations.tertius.Rule: Calculate the optimistic estimate of this rule.
calculateOutputs() - Method in class weka.classifiers.functions.MultilayerPerceptron: This will cause the output values of all the nodes to be calculated.
calculateRegionProbs(int, int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel.PlotThread
calculateRegionProbs(int, int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
calculateSE(boolean[], double[]) - Method in class weka.classifiers.functions.LinearRegression: Calculate the squared error of a regression model on the training data
calculateSphereSize(int, double, KStarWrapper) - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute: Calculates the size of the "sphere of influence" defined as: sphere = sum(P^2)/sum(P)^2 P(i|j) = (1-tstop)*P(i) + ((i==j)?
calculateSphereSize(double, KStarWrapper) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Calculates the size of the "sphere of influence" defined as: sphere = sum(P)^2/sum(P^2) where P(i) = root*exp(-2*i*root).
calculateStatistics(Instance, int, int, int) - Method in class weka.experiment.PairedCorrectedTTester: Computes a paired t-test comparison for a specified dataset between two resultsets.
calculateStatistics(Instance, int, int, int) - Method in class weka.experiment.PairedTTester: Computes a paired t-test comparison for a specified dataset between two resultsets.
calculateStdDevsTipText() - Method in class weka.experiment.AveragingResultProducer: Returns the tip text for this property
canCalculateConfirmation(Rule) - Method in class weka.associations.Tertius: Test if it is worth calculating the confirmation of a rule.
canCalculateOptimistic(Rule) - Method in class weka.associations.Tertius: Test if it is worth calculating the optimistic estimate of a rule.
canExplore(Rule) - Method in class weka.associations.Tertius: Test if a rule can be explored (if it is interesting for the results or for refining).
canHandleMissing(boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier: Checks basic missing value handling of the scheme.
canHandleNClasses(boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier: Checks whether nominal schemes can handle more than two classes.
canHandleZeroTraining(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Checks whether the scheme can handle zero training instances.
canKeep(Instance, Literal) - Method in class weka.associations.tertius.Body: Test if an instance can be kept as a counter-instance, if a new literal is added to this body.
canKeep(Instance, Literal) - Method in class weka.associations.tertius.Head: Test if an instance can be kept as a counter-instance, if a new literal is added to this head.
canKeep(Instance, Literal) - Method in class weka.associations.tertius.LiteralSet: Test if an instance can be kept as a counter-instance, given a new literal.
canPredict(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Checks basic prediction of the scheme, for simple non-troublesome datasets.
canRefine(Rule) - Method in class weka.associations.Tertius: Test if it is worth refining a rule.
canStoreInNodes(Rule) - Method in class weka.associations.Tertius: Test if a rule can be stored in the agenda.
canStoreInResults(Rule) - Method in class weka.associations.Tertius: Test if a rule can be added to the results.
canTakeOptions() - Method in class weka.classifiers.CheckClassifier: Checks whether the scheme can take command line options.
cancelGeneralisation() - Method in class weka.classifiers.rules.NNge.Exemplar: Cancels a generalisation started with preGeneralise.
cancelShapes() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: Sets the list of shapes to empty and also cancels the current shape being drawn (if applicable).
capacity() - Method in class weka.classifiers.functions.pace.DoubleVector: Gets the capacity of the vector.
capacity() - Method in class weka.classifiers.functions.pace.IntVector: Returns the capacity of the vector
capacity() - Method in class weka.core.FastVector: Returns the capacity of the vector.
cat(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Combine two vectors together
categ - Variable in class weka.classifiers.misc.FLR.FuzzyLattice
categoryUtility() - Method in class weka.clusterers.Cobweb.CNode: Computes the utility of all children with respect to this node
categoryUtilityChild(Cobweb.CNode) - Method in class weka.clusterers.Cobweb.CNode: Computes the utility of a single child with respect to this node
cbind(PaceMatrix) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns a new matrix which binds two matrices with columns.
cellRange - Variable in class weka.gui.visualize.MatrixPanel.Plot
cellSize - Variable in class weka.gui.visualize.MatrixPanel.Plot
changeFontSize(int) - Method in class weka.gui.treevisualizer.TreeVisualizer: This will change the font size for displaying the tree to the one specified.
changeInputNum(int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection: Changes the connection value information for one of the connections.
changeOutputNum(int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection: Changes the connection value information for one of the connections.
changeValueRandomly(Random, int, int, Instance, boolean) - Method in class weka.filters.unsupervised.attribute.AddNoise: method to set a new value
check(double) - Method in class weka.classifiers.trees.j48.Distribution: Checks if at least two bags contain a minimum number of instances.
checkAlphas() - Method in class weka.classifiers.functions.SMOreg: Debuggage function Checks that : alpha*alpha_=0 sum(alpha[i] - alpha_[i]) = 0
checkBest() - Method in class weka.attributeSelection.GeneticSearch: checks to see if any population members in the current population are better than the best found so far.
checkBounds() - Method in class weka.classifiers.misc.FLR: Checks the metric space
checkClassifier() - Method in class weka.classifiers.functions.SMO.BinarySMO: Quick and dirty check whether the quadratic programming problem is solved.
checkErrorRateTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
checkExit() - Method in class weka.gui.GUIChooser: Kills the JVM if all windows have been closed.
checkForAllFailedHosts() - Method in class weka.experiment.RemoteExperiment: Check to see if we have failed to connect to all hosts
checkForAllFailedHosts() - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed: Check to see if we have failed to connect to all hosts
checkForDuplicateKeys(Object[]) - Method in class weka.experiment.AveragingResultProducer: Checks whether any duplicate results (with respect to a key template) were received.
checkForInstance(Instances) - Method in class weka.classifiers.meta.ThresholdSelector: Checks whether instance of designated class is in subset.
checkForMissing(Instances) - Method in class weka.classifiers.functions.PaceRegression: Checks if instances have a missing value.
checkForMissing(Instance, Instances) - Method in class weka.classifiers.functions.PaceRegression: Checks if an instance has a missing value.
checkForMultipleDifferences() - Method in class weka.experiment.AveragingResultProducer: Checks that the keys for a run only differ in one key field.
checkForNonBinary(Instances) - Method in class weka.classifiers.functions.PaceRegression: Checks if any of the nominal attributes is non-binary.
checkForRemainingOptions(String[]) - Static method in class weka.core.Utils: Checks if the given array contains any non-empty options.
checkForStringAttributes() - Method in class weka.core.Instances: Checks for string attributes in the dataset
checkInstance(Instance) - Method in class weka.core.Instances: Checks if the given instance is compatible with this dataset.
checkMatrixDimensions(Matrix) - Method in class weka.classifiers.functions.pace.Matrix: Check if size(A) == size(B)
checkModel() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Checks if generated model is valid.
checkModel(int) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Checks if there are at least 2 subsets that contain >= minNumInstances.
checkOnClassifierType() - Method in class weka.gui.beans.ClassifierCustomizer
checkOptimality() - Method in class weka.classifiers.functions.SMOreg: Debuggage function.
checkOptionHandler(OptionHandler, String[]) - Static method in class weka.core.CheckOptionHandler: Runs some diagnostic tests on an optionhandler object.
checkPoints(double, double) - Method in class weka.gui.visualize.Plot2D: This will check the values of the screen points passed and make sure that they land on the screen
checkPoints(double, double) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel: This will check the values of the screen points passed and make sure that they land on the screen
checkSets() - Method in class weka.classifiers.functions.SMOreg: Debuggage function.
checkStatus(Object) - Method in interface weka.experiment.Compute: Check on the status of a Task
checkStatus(Object) - Method in class weka.experiment.RemoteEngine: Returns status information on a particular task
checkStop(double[], double, double) - Method in class weka.classifiers.rules.JRip: Check whether the stopping criterion meets
checkStructure(FastVector) - Method in class weka.core.converters.CSVLoader: Checks the current instance against what is known about the structure of the data set so far.
chiCell(double, double, boolean) - Static method in class weka.core.ContingencyTables: Computes chi-value for one cell in a contingency table.
chiSquared(double[][], boolean) - Static method in class weka.core.ContingencyTables: Returns chi-squared probability for a given matrix.
chiSquaredProbability(double, double) - Static method in class weka.core.Statistics: Returns chi-squared probability for given value and degrees of freedom.
chiVal(double[][], boolean) - Static method in class weka.core.ContingencyTables: Computes chi-squared statistic for a contingency table.
childInv(Node) - Method in class weka.gui.treevisualizer.Node: Recursively goes through the tree and sets all the children to invisible, Not the parent though.
childVis(Node) - Method in class weka.gui.treevisualizer.Node: Recursively goes through the tree and sets all the children and the parent to visible.
children - Variable in class weka.classifiers.trees.adtree.PredictionNode: The children of this node - any number of splitter nodes
children() - Method in class weka.classifiers.trees.adtree.PredictionNode: Enumerates the children of this node.
children - Variable in class weka.classifiers.trees.adtree.TwoWayNominalSplit: The children of this split
children - Variable in class weka.classifiers.trees.adtree.TwoWayNumericSplit: The children of this split
children - Variable in class weka.core.ClassTree: The subtrees which are children of this node of the hierarchy.
children - Variable in class weka.gui.HierarchyPropertyParser.TreeNode: The children of this node
childrenHNDs - Variable in class weka.classifiers.meta.HND: The HNDs covering the instances at deeper levels, if there are such.
childrenValues() - Method in class weka.gui.HierarchyPropertyParser: The value in the children nodes.
chisqDistribution - Static variable in class weka.classifiers.functions.pace.Maths: Distribution type: chi-squared
chooseDestinationFile() - Method in class weka.gui.experiment.SimpleSetupPanel: Lets user browse for a destination file..
chooseIndex() - Method in class weka.classifiers.rules.part.ClassifierDecList: Method for choosing a subset to expand.
chooseLastIndex() - Method in class weka.classifiers.rules.part.ClassifierDecList: Choose last index (ie. choose rule).
chooseURLUsername() - Method in class weka.gui.experiment.SimpleSetupPanel: Lets user enter username/password/URL.
chunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
classAttribute() - Method in class weka.core.Instance: Returns class attribute.
classAttribute() - Method in class weka.core.Instances: Returns the class attribute.
classAttributeNames() - Method in class weka.classifiers.functions.SMO
classColumnTipText() - Method in class weka.gui.beans.ClassAssigner: Tool tip text for this property
classFirst(boolean) - Method in class weka.experiment.Experiment: Sets whether the first attribute is treated as the class for all datasets involved in the experiment.
classHierarchyParser - Variable in class weka.classifiers.meta.HND: The default parser is a ClassTreeArffFileParser, suitable for a hierarchy encoded within a single String.
classIndex() - Method in class weka.core.Instance: Returns the class attribute's index.
classIndex() - Method in class weka.core.Instances: Returns the class attribute's index.
classIndex - Variable in class weka.gui.AttributeVisualizationPanel
classIndexTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
classIndexTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
classIsMissing() - Method in class weka.core.Instance: Tests if an instance's class is missing.
className(int) - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the name of one of the classes.
className - Variable in class weka.classifiers.misc.FLR.FuzzyLattice
classNameTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns the tip text for this property
classNames - Variable in class weka.classifiers.misc.FLR
classNames - Variable in class weka.core.converters.ClassTreeParser: Provides the class-names of a nominal class-attribute after initialization with the init(Instances)-method.
classOrderTipText() - Method in class weka.filters.supervised.attribute.ClassOrder: Returns the tip text for this property
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.BinC45Split: Gets class probability for instance.
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.C45Split: Gets class probability for instance.
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Gets class probability for instance.
classProbLaplace(int, Instance, int) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Gets class probability for instance.
classRemoved - Variable in class weka.core.ClassRemoveableInstances
classRemoved - Variable in class weka.core.Instances: Should be set to true, if the class-attribute was removed, and to false, if the class-attribute was added again.
classSelected(String) - Method in class weka.gui.GenericObjectEditor: Called when the user selects an class type to change to.
classValue() - Method in class weka.classifiers.rules.NNge.Exemplar: Return the class of the Exemplar
classValue() - Method in class weka.core.Instance: Returns an instance's class value in internal format.
classValues() - Method in class weka.classifiers.lazy.KStar: Note: for Nominal Class Only!
classificationTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
classifierTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns the tip text for this property
classifierTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.SingleClassifierEnhancer: Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.meta.AdditiveRegression: Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
classifierTipText() - Method in class weka.classifiers.meta.Decorate: Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.meta.FilteredClassifier: Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
classifierTipText() - Method in class weka.classifiers.meta.OrdinalClassClassifier
classifierTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
classifierTipText() - Method in class weka.classifiers.meta.ThresholdSelector
classifierTipText() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns the tip text for this property
classifierTipText() - Method in class weka.experiment.RegressionSplitEvaluator: Returns the tip text for this property
classifierTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
classifiers() - Method in class weka.classifiers.meta.LogitBoost: Returns the array of classifiers that have been built.
classifiersTipText() - Method in class weka.classifiers.MultipleClassifiersCombiner: Returns the tip text for this property
classifiersTipText() - Method in class weka.classifiers.meta.MultiScheme: Returns the tip text for this property
classify(Ridor.Ridor_node, Instance) - Method in class weka.classifiers.rules.Ridor: Classify the test instance with one node of Ridor
classifyExample(Instance) - Method in class weka.datagenerators.RDG1: Tries to classify an example.
classifyFoldCV(Instances, int[]) - Method in class weka.classifiers.rules.DecisionTable: Calculates the accuracy on a test fold for internal cross validation of feature sets
classifyInstance(Instance) - Method in class weka.classifiers.Classifier: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.LeastMedSq: Classify a given instance using the best generated LinearRegression Classifier.
classifyInstance(Instance) - Method in class weka.classifiers.functions.LinearRegression: Classifies the given instance using the linear regression function.
classifyInstance(Instance) - Method in class weka.classifiers.functions.PaceRegression: Classifies the given instance using the linear regression function.
classifyInstance(Instance) - Method in class weka.classifiers.functions.SMOreg: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.SimpleLinearRegression: Generate a prediction for the supplied instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.Winnow: Outputs the prediction for the given instance.
classifyInstance(Instance) - Method in class weka.classifiers.lazy.IB1: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.AdditiveRegression: Classify an instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.MetaCost: Classifies a given test instance.
classifyInstance(double[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
classifyInstance(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
classifyInstance(Instance) - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns a predicted class for the test instance.
classifyInstance(Instance) - Method in class weka.classifiers.misc.FLR: Classifies a given instance using the FLR Classifier model
classifyInstance(Instance) - Method in class weka.classifiers.rules.NNge: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.OneR: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.PART: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.Prism: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.Ridor: Classify the test instance with the rule learner
classifyInstance(Instance) - Method in class weka.classifiers.rules.ZeroR: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.part.MakeDecList: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.Id3: Classifies a given test instance using the decision tree.
classifyInstance(Instance) - Method in class weka.classifiers.trees.J48: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.LMT: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.j48.ClassifierTree: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.M5Base: Calculates a prediction for an instance using a set of rules or an M5 model tree
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel: Predicts the class of the supplied instance using the linear model.
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.Rule: Calculates a prediction for an instance using this rule or M5 model tree
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.RuleNode: Classify an instance using this node.
classifyInstance(Instance) - Method in class weka.datagenerators.RDG1.RuleList
classifyInstanceLeaveOneOut(Instance, double[]) - Method in class weka.classifiers.rules.DecisionTable: Classifies an instance for internal leave one out cross validation of feature sets
clean() - Method in class weka.classifiers.functions.supportVector.Kernel: Frees the memory used by the kernel.
clean() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Frees the cache used by the kernel.
clean() - Method in class weka.classifiers.functions.supportVector.RBFKernel: Frees the cache used by the kernel.
cleanUpData(Instances) - Method in class weka.classifiers.functions.LeastMedSq: Cleans up data
cleanseCross(Instances) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Cleanses the data based on misclassifications when performing cross-validation.
cleanseTrain(Instances) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Cleanses the data based on misclassifications when used training data.
cleanup(Instances) - Method in class weka.classifiers.rules.part.ClassifierDecList: Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.j48.BinC45ModelSelection: Sets reference to training data to null.
cleanup() - Method in class weka.classifiers.trees.j48.C45ModelSelection: Sets reference to training data to null.
cleanup(Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree: Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.lmt.LMTNode: Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.lmt.LogisticBase: Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.lmt.ResidualModelSelection: Method not in use
clear() - Method in class weka.associations.tertius.SimpleLinkedList
clear(int) - Method in class weka.attributeSelection.GeneticSearch.GABitSet: unset a bit in the chromosome
clear() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Clears this hashtable so that it contains no keys.
clear() - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
clearOutput() - Method in class weka.gui.streams.InstanceViewer
clearTemps_and_EdgesFromNodes() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: This method removes the temporary nodes that were added to fill in the gaps, and removes all edges from all nodes in their edges[][] array
clone() - Method in class weka.associations.tertius.LiteralSet: Returns a shallow copy of this set.
clone() - Method in class weka.associations.tertius.Rule: Returns a shallow copy of this rule.
clone() - Method in class weka.attributeSelection.GeneticSearch.GABitSet: makes a copy of this GABitSet
clone() - Method in interface weka.classifiers.IterativeClassifier: Performs a deep copy of the classifier, and a reference copy of the training instances (or a deep copy if required).
clone() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Creates and returns a clone of this object.
clone() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Clones the discrete function
clone() - Method in class weka.classifiers.functions.pace.DoubleVector: Clones the DoubleVector object.
clone() - Method in class weka.classifiers.functions.pace.IntVector: Clones the IntVector object.
clone() - Method in class weka.classifiers.functions.pace.Matrix: Clone the Matrix object.
clone() - Method in class weka.classifiers.functions.pace.PaceMatrix: Clone the PaceMatrix object.
clone() - Method in class weka.classifiers.trees.ADTree: Creates a clone that is identical to the current tree, but is independent.
clone() - Method in class weka.classifiers.trees.adtree.PredictionNode: Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.Splitter: Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Clones this node.
clone() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Allows to clone a model (shallow copy).
clone() - Method in class weka.classifiers.trees.j48.Distribution: Clones distribution (Deep copy of distribution).
clone() - Method in class weka.core.Matrix: Creates and returns a clone of this object.
closed - Variable in class weka.core.ProtectedProperties
cls - Variable in class weka.classifiers.functions.Logistic.OptEng
clusterInstance(Instance) - Method in class weka.clusterers.Clusterer: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.Cobweb: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.FarthestFirst: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.SimpleKMeans: Classifies a given instance.
clusterPriors() - Method in class weka.clusterers.DensityBasedClusterer: Returns the prior probability of each cluster.
clusterPriors() - Method in class weka.clusterers.EM: Returns the cluster priors.
clusterPriors() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns the cluster priors.
clusterProcessedInstance(Instance) - Method in class weka.clusterers.FarthestFirst: clusters an instance that has been through the filters
clusterProcessedInstance(Instance) - Method in class weka.clusterers.SimpleKMeans: clusters an instance that has been through the filters
clusterResultsToString() - Method in class weka.clusterers.ClusterEvaluation: return the results of clustering.
clustererTipText() - Method in class weka.filters.unsupervised.attribute.AddCluster: Returns the tip text for this property
clustererTipText() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Returns a description of this option suitable for display as a tip text in the gui.
clusteringSeedTipText() - Method in class weka.classifiers.functions.RBFNetwork: Returns the tip text for this property
cochransCriterion(double[][]) - Static method in class weka.core.ContingencyTables: Tests if Cochran's criterion is fullfilled for the given contingency table.
codingCost() - Method in class weka.classifiers.trees.j48.C45Split: Returns coding cost for split (used in rule learner).
codingCost() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Returns coding costs of model.
coefficients() - Method in class weka.classifiers.functions.LinearRegression: Returns the coefficients for this linear model.
coefficients() - Method in class weka.classifiers.functions.PaceRegression: Returns the coefficients for this linear model.
coefficients() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel: Return the array of coefficients
collapse() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree: Collapses a tree to a node if training error doesn't increase.
columnNames - Variable in class weka.gui.graphvisualizer.GraphVisualizer.GraphVisualizerTableModel
columnResponseExplanation(PaceMatrix, IntVector, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the squared ks-th response value if the j-th column becomes the ks-th after orthogonal transformation.
combinations(int, int) - Static method in class weka.classifiers.functions.LeastMedSq: Produces the combination nCr
combinedDL(double, double) - Method in class weka.classifiers.rules.RuleStats: Compute the combined DL of the ruleset in this class, i.e. theory DL and data DL.
committeeSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
compactify() - Method in class weka.core.Instances: Compactifies the set of instances.
compare(Object, Object) - Method in class weka.classifiers.trees.lmt.CompareNode
compareDatasets(Instances, Instances) - Method in class weka.classifiers.CheckClassifier: Compare two datasets to see if they differ.
compareOptions(String[], String[]) - Static method in class weka.core.CheckOptionHandler: Compares the two given sets of options.
comparisonString(int, Instances) - Method in class weka.classifiers.trees.adtree.Splitter: Gets the string describing the comparision the split depends on for a particular branch. i.e. the right hand side of the description of the split.
comparisonString(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Gets the string describing the comparision the split depends on for a particular branch. i.e. the right hand side of the description of the split.
comparisonString(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Gets the string describing the comparision the split depends on for a particular branch. i.e. the right hand side of the description of the split.
complexityParameterTipText() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a tip text for this property suitable for display in the GUI
computeAccu(Instances, int) - Method in class weka.classifiers.rules.ConjunctiveRule: Private function to compute number of accurate instances based on the specified predicted class
computeAverageClassValues() - Method in class weka.filters.supervised.attribute.NominalToBinary: Computes average class values for each attribute and value
computeBounds() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
computeCumulativeDistribution(double[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Return a cumulative distribution from a discrete distribution
computeDefAccu(Instances) - Method in class weka.classifiers.rules.JRip.RipperRule: Private function to compute default number of accurate instances in the specified data for the consequent of the rule
computeDefAccu(Instances) - Method in class weka.classifiers.rules.Ridor.RidorRule: Private function to compute default number of accurate instances in the specified data for m_Class
computeEntropy(Instances) - Method in class weka.classifiers.trees.Id3: Computes the entropy of a dataset.
computeError(Instances) - Method in class weka.classifiers.meta.Decorate: Computes the error in classification on the given data.
computeInfoGain(Instances, double, ConjunctiveRule.Antd) - Method in class weka.classifiers.rules.ConjunctiveRule: Compute the best information gain for the specified antecedent
computeInfoGain(Instances, double, JRip.Antd) - Method in class weka.classifiers.rules.JRip.RipperRule: Compute the best information gain for the specified antecedent
computeInfoGain(Instances, double, Ridor.Antd) - Method in class weka.classifiers.rules.Ridor.RidorRule: Compute the best information gain for the specified antecedent
computeInfoGain(Instances, Attribute) - Method in class weka.classifiers.trees.Id3: Computes information gain for an attribute.
computeMinMaxAtts() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
computeParams() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
computeSimpleStats(int, Instances, double[], double[]) - Method in class weka.classifiers.rules.RuleStats: Find all the instances in the dataset covered/not covered by the rule in given index, and the correponding simple statistics and predicted class distributions are stored in the given double array, which can be obtained by getSimpleStats() and getDistributions().
computeStats(Instances) - Method in class weka.classifiers.meta.Decorate: Compute and store statistics required for generating artificial data.
computeWeightedAcRt(double, double, double) - Method in class weka.classifiers.rules.Ridor.Ridor_node: Compute the weighted average of accuracy rate of a certain rule Each rule is weighted by its coverage proportion in the whole data.
conditionedZOnRows(double[][]) - Method in class weka.classifiers.trees.ADTree: Calculates the Z-value from the rows of a weight distribution array.
confidenceFactorTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
confidenceFactorTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
confidenceForRule(ItemSet, ItemSet) - Static method in class weka.associations.ItemSet: Outputs the confidence for a rule.
configureForClassAttribute() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Set up the class values combo boxes
confirmationComparator - Static variable in class weka.associations.tertius.Rule: Comparator used to compare two rules according to their confirmation value.
confirmationThenObservedComparator - Static variable in class weka.associations.tertius.Rule: Comparator used to compare two rules according to their confirmation and then their observed number of counter-instances.
confirmationThresholdTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
confirmationValuesTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
confusionMatrix() - Method in class weka.classifiers.Evaluation: Returns a copy of the confusion matrix.
connect(NeuralConnection, NeuralConnection) - Static method in class weka.classifiers.functions.neural.NeuralConnection: Connects two units together.
connectComponents(EventSetDescriptor, BeanInstance, int, int) - Method in class weka.gui.beans.KnowledgeFlow: Initiates the connection process for two beans
connectInput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralConnection: This will connect the specified unit to be an input to this unit.
connectInput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralNode: This will connect the specified unit to be an input to this unit.
connectOutput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralConnection: This will connect the specified unit to be an output to this unit.
connectToDatabase() - Method in class weka.experiment.DatabaseUtils: Opens a connection to the database
connectionAllowed(String) - Method in class weka.gui.beans.AbstractDataSink: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(String) - Method in class weka.gui.beans.AbstractEvaluator: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(String) - Method in class weka.gui.beans.AbstractTestSetProducer: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(String) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(String) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(String) - Method in interface weka.gui.beans.BeanCommon: Returns true if, at this time, the object will accept a connection via the named event
connectionAllowed(String) - Method in class weka.gui.beans.ClassAssigner: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(String) - Method in class weka.gui.beans.Classifier: Returns true if, at this time, the object will accept a connection with respect to the named event
connectionAllowed(String) - Method in class weka.gui.beans.Filter: Returns true if, at this time, the object will accept a connection with respect to the supplied event name
connectionAllowed(String) - Method in class weka.gui.beans.PredictionAppender: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(String) - Method in class weka.gui.beans.StripChart: Returns true if, at this time, the object will accept a connection via the named event
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractDataSink: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractEvaluator: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTestSetProducer: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in interface weka.gui.beans.BeanCommon: Notify this object that it has been registered as a listener with a source for recieving events described by the named event This object is responsible for recording this fact.
connectionNotification(String, Object) - Method in class weka.gui.beans.ClassAssigner: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.Classifier: Notify this object that it has been registered as a listener with a source with respect to the named event
connectionNotification(String, Object) - Method in class weka.gui.beans.Filter: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.PredictionAppender: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.StripChart: Notify this object that it has been registered as a listener with a source for recieving events described by the named event This object is responsible for recording this fact.
consistencyCount() - Method in class weka.attributeSelection.ConsistencySubsetEval: calculates the level of consistency in a dataset using a subset of features.
constructWithCopy(double[][]) - Static method in class weka.classifiers.functions.pace.Matrix: Construct a matrix from a copy of a 2-D array.
containedBy(Instance) - Method in class weka.associations.ItemSet: Checks if an instance contains an item set.
contains(Literal) - Method in class weka.associations.tertius.LiteralSet: Test if this LiteralSet contains a given Literal.
contains(int) - Method in class weka.classifiers.functions.supportVector.SMOset: Checks whether an element is in the set.
contains(int) - Method in class weka.classifiers.meta.ND.NDTree: Checks whether an index is in the array.
contains(Instances, int) - Static method in class weka.classifiers.rules.Prism: Does E contain any examples in the class C?
contains(Object) - Method in class weka.core.FastVector: added by akibriya
contains(String) - Method in class weka.gui.HierarchyPropertyParser: Whether the HierarchyPropertyParser contains the given string
containsKey(double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Tests if the specified double is a key in this hashtable.
containsKey(double) - Method in class weka.classifiers.lazy.kstar.KStarCache: Checks if the specified key maps with an entry in the cache table
contents(Object) - Method in class weka.core.Queue.QueueNode: Sets the contents of the node.
contents() - Method in class weka.core.Queue.QueueNode: Returns the contents in the node.
contents() - Method in class weka.experiment.PairedTTester.Dataset: Returns a vector containing the instances in the dataset
context - Variable in class weka.gui.HierarchyPropertyParser.TreeNode: The context of this node
context() - Method in class weka.gui.HierarchyPropertyParser: The context of the current node, i.e. the path from the root to the parent node of the current node, seperated by the seperator.
convertFromPanelX(double) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
convertFromPanelY(double) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
convertInfixToPostfix(String) - Method in class weka.filters.unsupervised.attribute.AddExpression: Converts a string containing a mathematical expression in infix form to postfix form.
convertInstance(Instance) - Method in interface weka.attributeSelection.AttributeTransformer: Transforms an instance in the format of the original data to the transformed space
convertInstance(Instance) - Method in class weka.attributeSelection.PrincipalComponents: Transform an instance in original (unormalized) format.
convertInstance(Instance) - Method in class weka.filters.supervised.attribute.AttributeSelection: Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.supervised.attribute.Discretize: Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.supervised.attribute.NominalToBinary: Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.AddCluster: Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.Discretize: Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Convert a single instance over if the class is nominal.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.Normalize: Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.NumericToBinary: Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.RandomProjection: converts a single instance to the required format
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.Standardize: Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
convertInstanceNominal(Instance) - Method in class weka.filters.supervised.attribute.NominalToBinary: Convert a single instance over if the class is nominal.
convertInstanceNumeric(Instance) - Method in class weka.filters.supervised.attribute.NominalToBinary: Convert a single instance over if the class is numeric.
convertInstanceToOriginal(Instance) - Method in class weka.attributeSelection.PrincipalComponents: Convert a pc transformed instance back to the original space
convertInstancewoDocNorm(Instance, FastVector) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
convertNewLines(String) - Static method in class weka.core.Utils: Converts carriage returns and new lines in a string into \r and \n.
convertNominalTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
convertToAttribX(double) - Method in class weka.gui.visualize.Plot2D: convert a Panel x coordinate to a raw x value.
convertToAttribY(double) - Method in class weka.gui.visualize.Plot2D: convert a Panel y coordinate to a raw y value.
convertToPanel(double) - Method in class weka.gui.visualize.AttributePanel.AttributeSpacing: Convert an raw x value to Panel x coordinate.
convertToPanelX(double) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
convertToPanelX(double) - Method in class weka.gui.visualize.Plot2D: Convert an raw x value to Panel x coordinate.
convertToPanelY(double) - Method in class weka.gui.beans.StripChart
convertToPanelY(double) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
convertToPanelY(double) - Method in class weka.gui.visualize.Plot2D: Convert an raw y value to Panel y coordinate.
convertToRelativePath(File) - Method in class weka.gui.experiment.DatasetListPanel: Converts a File's absolute path to a path relative to the user (ie start) directory
converterQuery(File) - Method in class weka.gui.explorer.PreprocessPanel: Pops up generic object editor with list of conversion filters
convictionForRule(ItemSet, ItemSet, int, int) - Method in class weka.associations.ItemSet: Outputs the conviction for a rule.
copy() - Method in class weka.associations.tertius.IndividualInstance
copy() - Method in class weka.classifiers.functions.pace.DoubleVector: Makes a deep copy of the vector
copy() - Method in class weka.classifiers.functions.pace.IntVector: Makes a deep copy of the vector
copy() - Method in class weka.classifiers.functions.pace.Matrix: Make a deep copy of a matrix
copy() - Method in class weka.classifiers.rules.JRip.Antd: Implements Copyable
copy() - Method in class weka.classifiers.rules.JRip.NominalAntd: Implements Copyable
copy() - Method in class weka.classifiers.rules.JRip.NumericAntd: Implements Copyable
copy() - Method in class weka.classifiers.rules.JRip.RipperRule: Get a shallow copy of this rule
copy() - Method in class weka.classifiers.rules.Rule: Get a shallow copy of this rule
copy() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Makes a copy of this CorrelationSplitInfo object
copy() - Method in interface weka.classifiers.trees.m5.SplitEvaluate: makes a copy of the SplitEvaluate object
copy() - Method in class weka.classifiers.trees.m5.YongSplitInfo: Makes a copy of this SplitInfo object
copy() - Method in class weka.core.Attribute: Produces a shallow copy of this attribute.
copy(String) - Method in class weka.core.Attribute: Produces a shallow copy of this attribute with a new name.
copy() - Method in class weka.core.BinarySparseInstance: Produces a shallow copy of this instance.
copy() - Method in interface weka.core.Copyable: This method produces a shallow copy of an object.
copy() - Method in class weka.core.FastVector: Produces a shallow copy of this vector.
copy() - Method in class weka.core.Instance: Produces a shallow copy of this instance.
copy() - Method in class weka.core.SparseInstance: Produces a shallow copy of this instance.
copy2DArray(int[][], int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Copies one array of type int[][] to another.
copyElements() - Method in class weka.core.FastVector: Clones the vector and shallow copies all its elements.
copyInstances(int, Instances, int) - Method in class weka.core.Instances: Copies instances from one set to the end of another one.
copyMatrix(int[][], int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Copies one Matrix of type int[][] to another.
copyObject(Object) - Method in class weka.gui.GenericObjectEditor.GOEPanel: Makes a copy of an object using serialization
copyObject(Object) - Method in class weka.gui.experiment.AlgorithmListPanel: Makes a copy of an object using serialization
copyStringValues(Instance, Instances, int[]) - Method in class weka.filters.Filter: Copies string values contained in the instance copied to a new dataset.
copyStringValues(Instance, boolean, Instances, Instances) - Method in class weka.filters.Filter: Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
copyStringValues(Instance, boolean, Instances, int[], Instances, int[]) - Method in class weka.filters.Filter: Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
correct() - Method in class weka.classifiers.Evaluation: Gets the number of instances correctly classified (that is, for which a correct prediction was made).
correct() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the number of correct classifications (that is, for which a correct prediction was made).
correctBuildInitialisation(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier: Checks whether the scheme correctly initialises models when buildClassifier is called.
correlate(int, int) - Method in class weka.attributeSelection.CfsSubsetEval
correlation(double[], double[], int) - Static method in class weka.core.Utils: Returns the correlation coefficient of two double vectors.
correlation - Variable in class weka.experiment.PairedStats: The correlation coefficient
correlationCoefficient() - Method in class weka.classifiers.Evaluation: Returns the correlation coefficient if the class is numeric.
costMatrix - Variable in class weka.core.converters.HierarchicalCostMatrix: Keeps the cost matrix.
costMatrixSourceTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
costMatrixSourceTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
costMatrixTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
costMatrixTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
count - Variable in class weka.experiment.PairedStats: The number of data points seen
count - Variable in class weka.experiment.Stats: The number of values seen
count - Variable in class weka.filters.unsupervised.attribute.StringToWordVector.Count
countData() - Method in class weka.classifiers.rules.RuleStats: Filter the data according to the ruleset and compute the basic stats: coverage/uncoverage, true/false positive/negatives of each rule
countData(int, Instances, double[][]) - Method in class weka.classifiers.rules.RuleStats: Count data from the position index in the ruleset assuming that given data are not covered by the rules in position 0...
countFeatures(BitSet) - Method in class weka.attributeSelection.ExhaustiveSearch: counts the number of features in a subset
countFeatures(BitSet) - Method in class weka.attributeSelection.GeneticSearch: counts the number of features in a subset
countFeatures(BitSet) - Method in class weka.attributeSelection.RandomSearch: counts the number of features in a subset
count_Lab - Variable in class weka.gui.streams.InstanceSavePanel
counterInstance(Instance, Instance) - Method in class weka.associations.tertius.LiteralSet: Test if an individual instance, given a part instance of this individual, is a counter-instance of this LiteralSet.
counterInstance(Instance) - Method in class weka.associations.tertius.LiteralSet: Test if an instance is a counter-instance of this LiteralSet.
counterInstance(Instance) - Method in class weka.associations.tertius.Rule: Test if an instance is a counter-instance of this rule.
countsForInstance(Instance) - Method in class weka.classifiers.bayes.BayesNet: Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance.
cover - Variable in class weka.classifiers.rules.JRip.Antd
cover - Variable in class weka.classifiers.rules.Ridor.Antd
coverage - Variable in class weka.classifiers.rules.ConjunctiveRule.NominalAntd
coverage - Variable in class weka.classifiers.rules.JRip.NominalAntd
coverage - Variable in class weka.classifiers.rules.Ridor.NominalAntd
coveredBy(Instances) - Method in class weka.classifiers.rules.Prism.PrismRule: Returns the set of instances that are covered by this rule.
coveredByRule(Instances) - Method in class weka.classifiers.rules.Ridor.RidorRule: Find all the instances in the dataset covered by this rule.
covers(Instance) - Method in class weka.classifiers.rules.JRip.Antd
covers(Instance) - Method in class weka.classifiers.rules.JRip.NominalAntd: Whether the instance is covered by this antecedent
covers(Instance) - Method in class weka.classifiers.rules.JRip.NumericAntd: Whether the instance is covered by this antecedent
covers(Instance) - Method in class weka.classifiers.rules.JRip.RipperRule: Whether the instance covered by this rule
covers(Instance) - Method in class weka.classifiers.rules.Rule: Whether the instance covered by this rule
create(Reader) - Method in class weka.gui.treevisualizer.TreeBuild: This will build A node structure from the dotty format passed.
createChooseClassButton() - Method in class weka.gui.GenericObjectEditor: Creates a button that when clicked will enable the user to change the class of the object being edited.
createDefaultPanel() - Method in class weka.gui.PropertyPanel: Creates the default style of panel for editors that do not supply their own.
createExperimentIndex() - Method in class weka.experiment.DatabaseUtils: Attempts to create the experiment index table
createExperimentIndexEntry(ResultProducer) - Method in class weka.experiment.DatabaseUtils: Attempts to insert a results entry for the table into the experiment index.
createFileChooser() - Method in class weka.gui.GenericObjectEditor.GOEPanel: Creates the file chooser the user will use to save/load files with.
createKey(Instances) - Method in class weka.core.ClassTree: Provides a String as unique key for a ClassTree with respect to the given Instances.
createNodes(DefaultMutableTreeNode) - Method in class weka.gui.PropertySelectorDialog: Creates the property tree below the current node.
createOptions() - Method in class weka.classifiers.meta.CVParameterSelection: Create the options array to pass to the classifier.
createResultsTable(ResultProducer, String) - Method in class weka.experiment.DatabaseUtils: Creates a results table for the supplied result producer.
createSubsample() - Method in class weka.filters.supervised.instance.Resample: Creates a subsample of the current set of input instances.
createSubsample() - Method in class weka.filters.supervised.instance.SpreadSubsample: Creates a subsample of the current set of input instances.
createSubsample() - Method in class weka.filters.unsupervised.instance.Resample: Creates a subsample of the current set of input instances.
createTree(HierarchyPropertyParser) - Method in class weka.gui.GenericObjectEditor: Creates a JTree from an object heirarchy.
crossValTipText() - Method in class weka.classifiers.rules.DecisionTable: Returns the tip text for this property
crossValidate() - Method in class weka.classifiers.lazy.IBk: Select the best value for k by hold-one-out cross-validation.
crossValidateModel(Classifier, Instances, int, Random) - Method in class weka.classifiers.Evaluation: Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(String, Instances, int, String[], Random) - Method in class weka.classifiers.Evaluation: Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(String, Instances, int, String[], Random) - Static method in class weka.clusterers.ClusterEvaluation: Performs a cross-validation for a distribution clusterer on a set of instances.
crossValidateTipText() - Method in class weka.classifiers.lazy.IBk: Returns the tip text for this property
crossings(int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Computes the number of edge crossings in the whole graph Takes as an argument levels of nodes.
crossoverProbTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
cuScoreForBestTwoMerged(Cobweb.CNode, Cobweb.CNode, Cobweb.CNode, Instance) - Method in class weka.clusterers.Cobweb.CNode
cuScoresForChildren(Instance) - Method in class weka.clusterers.Cobweb.CNode: Temporarily adds a new instance to each of this nodes children in turn and computes the category utility.
cumulate() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns a vector that stores the cumulated values of the original vector
cumulateInPlace() - Method in class weka.classifiers.functions.pace.DoubleVector: Cumulates the original vector in place
current - Variable in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
current - Variable in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
currentLength() - Method in class weka.classifiers.lazy.IBk.NeighborList: Gets the current length of the list.
currentPos - Variable in class weka.filters.unsupervised.attribute.StringToWordVector.AlphabeticStringTokenizer
cutPointsForSubset(Instances, int, int, int) - Method in class weka.filters.supervised.attribute.Discretize: Selects cutpoints for sorted subset.
cutoffTipText() - Method in class weka.clusterers.Cobweb: Returns the tip text for this property

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